Three fixes for Docker-native install with --memory:
1. Add sqlite-vec to proxy extras — memory's vector index now installs
with pip install headroom-ai[proxy]. No separate pip install needed.
2. Fix AUTO vector backend fallback — was: SQLITE_VEC → HNSW → crash.
Now: SQLITE_VEC → HNSW → clear error message listing install options.
3. Fix ONNX pthread_setaffinity_np error in Docker containers — set
intra/inter thread count to 1 in SessionOptions. Prevents the
"Invalid argument" error on containers with limited CPU affinity.
No manual install needed. headroom wrap claude --code-graph:
1. Checks if binary exists (PATH or ~/.local/bin/)
2. Downloads from GitHub releases if not found (~15MB, one-time)
3. Indexes project + starts file watcher
Same pattern as rtk auto-download. Platform-aware:
darwin-arm64, darwin-amd64, linux-arm64, linux-amd64, windows-amd64.
Also: add watchdog>=4.0.0 to proxy extras.
Add a default devcontainer and a compose-backed memory-stack profile, validate them in CI, and document the contributor workflow.
Also lock the memory-stack dependencies, pin related container tooling, and sync the latest healthcheck shutdown fix for stubbed memory handlers.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Add a shared observability layer for OTEL metrics and Langfuse tracing.
Instrument the shared compression pipeline once so SDK and proxy paths can
reuse the same metrics and spans without scattering telemetry concerns.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Transcode technique now runs RapidOCR to extract text from images.
Falls back to full_low if OCR confidence < 70% or no text detected.
Token counting is now done AFTER compression by measuring the actual
output — no hardcoded estimates. OCR text counted by char length,
resized images re-estimated from new dimensions.
RapidOCR added to headroom-ai[image] extra (~15MB ONNX models, ~180ms CPU).
4 new OCR tests (extraction, blank image, confidence threshold, full pipeline).
29 total image compression tests passing.
SiglipTokenizer from transformers hard-requires sentencepiece at runtime.
Without it, the image router silently falls back to PRESERVE (no compression).
Users on pip install headroom-ai[image] or [all] were missing this dependency.
- Fix beacon spam: file lock ensures only one beacon per proxy regardless
of worker count. Workers > 1 caused N beacons firing N rows per cycle.
- Beacon upsert: on_conflict=session_id prevents duplicate rows.
- Beacon stop() guard: skip final report if uptime < 2 minutes.
- Fix dashboard cost: savings_usd now uses model list price (monotonic),
not moving average. Separate breakdown for compression/cache/rtk.
Fixes#83
KompressCompressor now tries ONNX Runtime first (156MB INT8 model),
falls back to PyTorch only if ONNX unavailable. No torch needed for
text compression — just onnxruntime (~50MB) + transformers (tokenizer).
Changes:
- Add onnxruntime + transformers to [proxy] extra in pyproject.toml
- Add _OnnxModel wrapper with get_scores/get_keep_mask interface
- _load_kompress() tries ONNX first, falls back to PyTorch
- is_kompress_available() returns True if EITHER backend available
- compress() handles both numpy (ONNX) and tensor (PyTorch) outputs
Dependency impact:
Before: pip install headroom-ai[proxy] → no text compression
After: pip install headroom-ai[proxy] → Kompress ONNX INT8 (156MB)
[ml] extra still available for full PyTorch (600MB, GPU support)
Codex v0.117.0+ with newer models uses WebSocket instead of HTTP POST
for the Responses API. Added @app.websocket("/v1/responses") handler that:
- Accepts ws:// connections and forwards to wss://api.openai.com
- Compresses input on first message using existing pipeline
- Relays all response events bidirectionally
- Handles SSL (certifi), graceful disconnect, missing websockets lib
Tested with real OpenAI API: basic text, large tool outputs (200 records),
parallel function calls, instructions preservation.
Addresses #79
Use stream_reader instead of decompress() for zstd request bodies.
Codex uses streaming zstd (no content size in frame header), which
causes decompress() to fail. stream_reader handles both cases.
Also added zstandard to the [proxy] optional dependencies.
Performance:
- Replace json roundtrip with copy.deepcopy in deep_copy_messages (~20-80ms)
- Eliminate redundant token counting in pipeline (8→2 calls, ~30-100ms)
- Parallel message compression in ContentRouter via ThreadPoolExecutor (~100-200ms)
- Add granular timing metrics: deep_copy, token_count, parallel_compress
- Switch hot-path hashing from SHA256 to MD5 (2-3x faster, non-crypto)
Telemetry:
- Enrich beacon payload with overhead, TTFB, pipeline timing, request
patterns, compression cache stats, CCR usage, and waste signals
- Each extraction section guarded independently so one bad key never
blocks the rest; Supabase POST failure never affects proxy
Bug fix:
- Add Bash/bash to DEFAULT_EXCLUDE_TOOLS so tree/ls output is not
compressed by the text compressor
SmartCrusher: eliminate 5-7x redundant json.dumps by threading cached item_strings
through _crush_array → _create_plan → _plan_* methods, TOIN token counting, and CCR
storage. Move ISO datetime regex to module level. Cache field name hashes in TOIN
semantic detection. Add item_strings param to error detection.
ContentRouter: compile prose detection regex at module level. Extend
eager_load_compressors() to pre-load Magika detector, tree-sitter parsers (8 common
languages), CodeAwareCompressor, and SmartCrusher at startup.
Magika: add as proxy dependency (was never declared in pyproject.toml). Update
detector.py for Magika 1.x API (result.output.label, result.score). Fix batch
detection to use identify_bytes loop (identify_bytes_batch removed in 1.x).
Proxy: simplify startup to use eager_load_compressors() return status dict for
unified component logging.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add anonymous telemetry beacon: proxy sends aggregate-only stats
(tokens saved, compression ratios, cache hit rates) to Supabase
every 5 minutes. No prompts, no content, no PII. Opt out with
--no-telemetry or HEADROOM_TELEMETRY=off.
- Fix Starlette 0.41+ TypeError: migrate from deprecated on_event
decorators to lifespan context manager. Fixes crash with
"Router.__init__() got an unexpected keyword argument 'on_startup'".
- Bump version to 0.5.4.
- Fix proxy crash when torch not installed: make kompress_compressor.py
imports lazy so `is_kompress_available()` works without [ml] extra
- Rewrite MCP server from 1 tool (retrieve-only) to 3 tools:
headroom_compress (on-demand compression, no proxy needed),
headroom_retrieve (local store first, proxy fallback),
headroom_stats (session stats + sub-agent aggregation + proxy cache)
- Add shared stats file (~/.headroom/session_stats.jsonl) so sub-agent
compression stats are visible from the main session
- Add mcp to [proxy] extras so proxy users get MCP tools automatically
- Remove dead TextCompressor from exports and pipeline (was never called)
- Update mcp install messaging to clarify proxy vs MCP roles
- Fix fcntl Windows compat, asyncio deprecation, httpx timeout race
Bump version to 0.4.6.
The proxy startup crashed with `ModuleNotFoundError: No module named
'torch'` when installed with just `[proxy]` extras because
kompress_compressor.py had unconditional top-level torch imports.
Moved torch/transformers imports to be lazy so the module is safely
importable without the [ml] extra. Added tests for import safety.
Bumped version to 0.4.5
Move heavy deps to optional extras: sentence-transformers, torch,
numpy, pillow, datasets, accelerate out of core. Remove unused deps
entirely (semantic-router, protobuf, sentencepiece).
New extras: [ml] for Kompress, [image] for image compression,
[langchain] for LangChain integration. Guard memory/image imports
so core install works without numpy/torch.
Core (tiktoken, pydantic, litellm, click, rich) gives full
compression: SmartCrusher, ContentRouter, CCR, TOIN, CLI.
- CLI now reads OPENAI_TARGET_API_URL, GEMINI_TARGET_API_URL, and
HEADROOM_ANYLLM_PROVIDER environment variables
- Add --openai-api-url and --gemini-api-url CLI flags
- Remove --backend choices restriction so litellm-* backends work
- Forward tools/tool_choice through LiteLLM and any-llm backends
- Parse tool call arguments from JSON string to dict (Anthropic format)
- Forward top_p, stop_sequences, tools in streaming paths
- Update Vertex AI model map with Claude 3 through 4.6 (from official docs)
- Bump version to 0.4.1
